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Discovery of Inference Rules for Question Answering. Dekang Lin and Patrick Pantel Natural Language Engineering 7(4):343-360, 2001 as (mis-)interpreted by Peter Clark. Goal. Observation: “mismatch” between expressions in qns and text e.g. “X writes Y” vs. “X is the author of Y”

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discovery of inference rules for question answering

Discovery of Inference Rules for Question Answering

Dekang Lin and Patrick Pantel

Natural Language Engineering 7(4):343-360, 2001

as (mis-)interpreted by Peter Clark

slide2
Goal
  • Observation:
    • “mismatch” between expressions in qns and text
      • e.g. “X writes Y” vs. “X is the author of Y”
  • Need “inference rules” to answer questions
      • “X writes Y”  “X is the author of Y”
      • “X manufactures Y”  “X’s Y factory”
  • Question:
    • Can we learn these inference rules from text?
      • (aka “paraphrases”, “variants”)
    • DIRT (Discovering Inference Rules from Text)
the limits of word search
The limits of word search…
  • Who is the author of ‘Star Spangled Banner?’

A.

…Francis Scott Key wrote the “Star Spangled Banner” in 1814.

…comedian-acress Roseanne Barr sang her famous shrieking rendition of the “Star Spangled Banner” before a San Diego Padres-Cincinnati Reds game.

B.

  • What does Peugot manufacture?

Chrétien visited Peugot’s newly renovated car factory in the afternoon.

approach
Approach
  • Parse sentences in a giant (1GB) corpus
  • Extract instantiated “paths” from the parse tree, e.g.:
    • X buys something from Y
    • X manufactures Y
    • X’s Y factory
  • For each path, collect the sets of X’s and Y’s
  • For a given path (pattern), find other paths where the X’s and Y’s are pretty similar
approach1
Approach
  • Parse sentences in a giant (1GB) corpus, then:
  • Extract “paths” from the parse tree, e.g.:
    • X buys something from Y
    • X manufactures Y
    • X’s Y factory
  • Collect statistics on what the X’s and Y’s are
  • Compare the X-Y sets:
    • For a given path (pattern), find other paths where the X’s and Y’s are similar
method 1 parse corpus
Method: 1. Parse Corpus
  • 1GB newspaper (Reuters?) corpus
  • Use MiniPar
    • Chart parser
    • self-trained statistical ranking of parse (“dependency”) trees
method 4 compare the x y sets2
Method: 4. Compare the X-Y sets

1. Characterizing a single X-Y set:

  • Count frequencies of words for X (and Y)
  • Weight by ‘saliency’ (slot-X mutual information)
method 4 compare the x y sets3
Method: 4. Compare the X-Y sets

2. Comparing two X-Y sets

  • Two paths have high similarity if there are a large number of common features.
  • Mathematically:
slide14

Example:

Learned

Inference

rules

slide15

Example:

vs. Hand-crafted

inference

rules (by ISI)

observations
Observations
  • Little overlap in manual and automatic rules
  • DIRT performance varies a lot
    • Much better with verb rather than noun roots
    • If less than 2 modifiers, no paths found
  • For some TREC examples, no “correct” rules found
    • “X leaves Y”  “X flees Y”
  • Where X’s and Y’s are similar, can get agent-patient the wrong way round
    • E.g. “X asks Y” vs. “Y asks X”
the big question
The Big Question
  • Can we acquire the vast amount of common-sense knowledge from text?
  • Lin and Pantel suggests: “yes” (at least in a semi-automated way)